Mining of instant messaging data in the Internet of Things based on support vector machine

With the development and popularization of mobile communication technology, mobile communication has been applied in more and more industries, and has effectively promoted the progress of the times. How to take effective methods and means to ensure the correct classification and mining of effective instant messaging information has become a top priority for major operators. Based on the analysis of the theory and feature selection technology of support vector machine in machine learning field, this paper studies the main parameters that affect the feature selection of support vector machine, aiming at the dimension disaster caused by large-scale data volume and redundant features of the Internet of things. In this paper, an algorithm is proposed to optimize the feature subsets of samples, and then add the parameters of support vector machine to optimize the classification. The experimental results show that the algorithm has a good effect on the classification of effective instant messaging information of Internet of things big data, and has a good effect and practical application value.